{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db6e80af-30a0-461e-90c2-da2a142fe363",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bdb5d73-16e2-426e-95a7-2e0cb11056b0",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22685d7b-2054-4435-80dc-698359f5f016",
   "metadata": {},
   "outputs": [],
   "source": [
    "### constants \n",
    "R_M = 1737.4 * 1000.0 #m radius moon\n",
    "g_moon = 1.63 #m/s^2 gravity moon\n",
    "\n",
    "#### mass distirbution constants derived by xie 2020\n",
    "b_mt = 0.91\n",
    "C_mh = 0.013\n",
    "b_v = -5.7\n",
    "\n",
    "\n",
    "#### material properties\n",
    "# assume material properties similar to sand \n",
    "K1 = 1.03\n",
    "v = 0.4\n",
    "mu = 0.41\n",
    "Y = 10 #kPA material strength\n",
    "\n",
    "Y_u = Y*1000 #kPa convertred kg/ms \n",
    "\n",
    "R_sc = 9.5 * 1000 #simple-complex transition radius in m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600f29d6-c85d-4b86-a3ae-4baad59a50a3",
   "metadata": {},
   "source": [
    "## Ballistic sedimentation model\n",
    "\n",
    "As described in  (Haskin, 1998), (Haskin, 2003), (Xie et al. ,2020)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4c68668-f554-4f84-ae56-42085c8e5e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ejecta_thickness_bs(dist_r,Rt, theta_0 = 45,rho_m = 3000.,rho_t = 3000.,b = 0.98,cov = 0.5):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "        dist_r (ndarray 1xn): Distance from center of primary impact to center of SOI (km)\n",
    "        Rt(float): transient cavity radius of primary impact (360.5 for Imbrium)\n",
    "        theta_0 (int): ejection angle of primary fragments\n",
    "                       (default = 45)\n",
    "        rho_t (float): Density of target material in kg/m^3\n",
    "                       (Default = 3000. for high estimate of gabbroic anorthosite)\n",
    "        rho_t (float): Density of target material in kg/m^3\n",
    "                       (Default = 3000. for high estimate of gabbroic anorthosite)\n",
    "        b (float): fragmentation factor (default = 0.98 per Xie paper)\n",
    "        cov (float): Fraction of SOI to saturate w craters (default 0.5)\n",
    "    \n",
    "    Output:\n",
    "        th_soi_X (ndarray) = primary material thickness\n",
    "        loc_th (ndarray) = local material thickness\n",
    "        fin_th (ndarray) = total ejecta deposit\n",
    "    \"\"\"\n",
    "    dist_r_m = dist_r * 1000 #into meters\n",
    "\n",
    "    L_soi = 2.0*(dist_r**(0.5)) #m length of square of interest in km\n",
    "\n",
    "    L_soi_m = L_soi * 1000 # ^ in m\n",
    "\n",
    "    Rt *= 1000 #m into meters\n",
    "    theta_0 = np.deg2rad(theta_0) #ejection angle of primary fragments\n",
    "\n",
    "    R_at = Rt/1.2 #apparent transient radius ( Baldwin 1963, Pike 1974)\n",
    "\n",
    "   \n",
    "    ###calculating distance to SOI #######\n",
    "    \n",
    "    rs = lambda d: d - R_at   ### distance of launch point to center of SOI on flat\n",
    "\n",
    "    X = lambda d: rs(d)/(2.0*R_M)  \n",
    "    U = lambda d: np.sqrt(R_M*g_moon*np.tan(X(d)))/np.sqrt(np.tan(X(d))*np.cos(theta_0)**2 + np.sin(theta_0)*np.cos(theta_0)) #m/s Range-velocity eq. on spherical surface\n",
    "\n",
    "\n",
    "    ##disance traveled ring areas from Xie 2020\n",
    "    r_flat = lambda d: Rt + (U(d)**2)*np.sin(2*theta_0)/(g_moon) ###converting great circle distances (on sphere) to radial distance r_flat\n",
    "\n",
    "    r_inner =  r_flat(dist_r_m - L_soi_m/2)#m distance to inner edge of SOI\n",
    "    r_outer = r_flat(dist_r_m + L_soi_m/2)#m distance to outer edge of SOI\n",
    "\n",
    "    r_bar = np.sqrt(r_inner*r_outer) #geometric mean of distances in a given SOI\n",
    "\n",
    "    \n",
    "    ##Calculate SOI ring areas from Xie 2020\n",
    "    S_ring = 2*np.pi*(R_M**2)*(np.cos((dist_r_m - L_soi_m/2)/R_M) - np.cos((dist_r_m + L_soi_m/2)/R_M)) #m^2 (on a sphere)\n",
    "    S_ringflat = np.pi*(r_outer**2 - r_inner**2) ### m^2 on flat surface\n",
    "\n",
    "\n",
    "    th_soi_X = 0.068*R_at*((r_bar/R_at)**(-3))*(S_ringflat/S_ring) #m thickness of ejecta as function of distance on spherical target\n",
    "\n",
    "\n",
    "    M_soi = th_soi_X*rho_m*L_soi_m**2  ### kg mass of ejecta\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    #because scaling law in km, need to convert everything in meters\n",
    "    r_lsc = 1000*(7.21*(R_at/1000)**(0.94)) ##m distance to largest secondary crater (LSC)\n",
    "\n",
    "    M_t = 0.09*rho_m*np.pi*Rt**3 ###  (mass excavated from transient crater)\n",
    "\n",
    "\n",
    "    U_lsc = U(r_lsc) \n",
    " \n",
    "    idcs = np.where(U(dist_r) < U_lsc) #to avoid impractically large proximal ejecta\n",
    "    \n",
    "    \n",
    "    N = lambda m,C: C*m**(-b) #Number of fragments of sizes >m (cumulative ejecta) (description of mass distribution)\n",
    "\n",
    "\n",
    "    m_h = C_mh*(M_t**(b_mt))*(U(dist_r_m)/U_lsc)**(-b_v) ###upper limit of mass\n",
    "\n",
    "    m_h[idcs] = C_mh*M_t**(b_mt)\n",
    "    m_l = 10**(-18)*m_h ###lower limit of mass\n",
    "    C_soi = M_soi*(1-b)/(b*(m_h**(1-b)-m_l**(1-b))) ###constants describing mass distirbution\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    ### for each SOI\n",
    "\n",
    "    loc_th = np.zeros_like(th_soi_X) ##amount of local material ejected\n",
    "\n",
    "    for i in range(len(dist_r_m)):\n",
    "        ###discretize mass\n",
    "        ml = np.geomspace(m_l[i],m_h[i],1000)\n",
    "        mr = 1.05*ml \n",
    "        delm = ml - mr\n",
    "\n",
    "        m_int = np.sqrt(ml*mr)\n",
    "        ##number of fragments per mass \n",
    "        delN = C_soi[i]*(ml**(-b) - mr**(-b))\n",
    "\n",
    "        a = 2*np.power((3*m_int/(4.0*np.pi*rho_m)),1.0/3.0)\n",
    "        U_tan = U(dist_r_m[i])*np.sin(theta_0)\n",
    "\n",
    "        ## radii of secondary transient craters\n",
    "        R_at_s = K1*a*np.power((g_moon*a/U_tan**2)*np.power((rho_t/rho_m),\n",
    "                                                          2*v/mu) + np.power(Y_u/(rho_t*U_tan**2),\n",
    "                                                                             (2+mu)/2)*np.power(rho_t/rho_m,v*(2+mu)/mu),\n",
    "                             -mu/(2+mu))\n",
    "\n",
    "\n",
    "        ###excavation depth of a given crater \n",
    "        d_ex = 0.0134*R_at_s*U(dist_r_m[i])**(0.38) # excavation depth \n",
    "\n",
    "        ### maximum effective excavation \n",
    "        C_ex = 3.5 #determined by Xie et al. 2020\n",
    "        d_eff = C_ex*d_ex ##also = T_LM\n",
    "        ### layers \n",
    "\n",
    "\n",
    "        Th_layer = np.max([0.002*np.max(d_eff),0.01*th_soi_X[i]]) #trade-off between speed and accuracy\n",
    "\n",
    "        N_layer = np.ceil(th_soi_X[i]/Th_layer) #number of layers\n",
    "        Th_layeru = th_soi_X[i]/N_layer #updated thickness\n",
    "\n",
    "\n",
    "        ##for j-th arriving layer of ejecta\n",
    "        layyer = np.arange(1,N_layer +1,1) # enumerate the layers\n",
    "\n",
    "\n",
    "\n",
    "        TLM = C_ex*d_ex \n",
    "\n",
    "        temp_w = np.zeros_like(R_at_s)\n",
    "\n",
    "        tlm = C_ex*d_ex\n",
    "\n",
    "        S = L_soi_m[i]**2 ##area of interest area\n",
    "\n",
    "        W_TLM = np.zeros_like(TLM)         # fraction of pre-impact surface of area of the SOI covered by >= 1 crater w/ radius in R_at_s\n",
    "\n",
    "\n",
    "        for k in range(0,len(R_at_s)):\n",
    "            pre_th = (layyer-1)*th_soi_X[i]/N_layer\n",
    "            R_min = R_at_s[k]\n",
    "            tlm = TLM[k]\n",
    "\n",
    "            R_at_r = R_at_s[k:] # range of R_at from R_min to R_max\n",
    "            d_ex_r = 0.0134*R_at_r*U(dist_r_m[i])**(0.38) # excavation depth \n",
    "            delN_r = delN[k:]\n",
    "            temp_w = np.zeros_like(R_at_r) #setting up R-dependent summation\n",
    "\n",
    "\n",
    "            for j in range(len(R_at_r)):\n",
    "                S_PIS = np.zeros_like(layyer)\n",
    "                idcs = np.where(pre_th < C_ex*d_ex_r[j])\n",
    "                S_PIS[idcs] = np.pi*(R_at_r[j]**2)*(1-tlm/(C_ex*d_ex_r[j]))*(1- ((layyer[idcs] - 1)*th_soi_X[i])/(N_layer*C_ex*d_ex_r[j]))\n",
    "                temp_w[j] = np.sum(S_PIS)*delN_r[j]/N_layer\n",
    "            W_TLM[k] = 1.0 - np.exp(-np.sum(temp_w)/S)\n",
    "        loc_th[i] = TLM[np.argmin(np.abs(W_TLM - cov))] #50% coverage thickness (median)\n",
    "        print(\"done with {}/{}\".format(i,len(dist_r-1)),end=\"\\r\")\n",
    "    fin_th = th_soi_X + loc_th\n",
    "\n",
    "    return th_soi_X, loc_th, fin_th"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1797d00-c93a-4dc4-8816-f67acc8ec2dc",
   "metadata": {},
   "source": [
    "### Imbrium run\n",
    "\n",
    "For Imbrium basin with transient crater radius 360.5 (Miljkovic, 2016):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e081646-6305-40b9-a426-d6dc52db1e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_r = np.arange(800,5200,50) #km distance from center of primary impact to center of SOI (spacing of 50)\n",
    "R_t = 360.5\n",
    "\n",
    "primary,local,total = ejecta_thickness_bs(dist_r, R_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4722340-4ce3-455f-bf32-343d0d26313c",
   "metadata": {},
   "source": [
    "### Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4969d475-e770-4095-ba16-08c5e72aec02",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat_save = np.zeros((len(th_soi_X),4))\n",
    "dat_save[:,0] = dist_r\n",
    "dat_save[:,1] = fin_th\n",
    "dat_save[:,2] = th_soi_X\n",
    "dat_save[:,3] = loc_th\n",
    "\n",
    "np.savetxt('../results/imbrium721_45.txt',dat_save, header='Distance(km) Total_Thickness(m) Primary_Ejecta(m) Local_Materials(m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9452d0bb-2681-4c64-b8ba-92178b57698d",
   "metadata": {},
   "source": [
    "### Example Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "57930da2-cfc6-4200-8a2e-aeaa1f1aa150",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 563,
       "width": 847
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.family\"] = \"Helvetica\"\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(figsize = [14,9])\n",
    "ax.semilogy(dist_r,total/1000,  linewidth = 4, color = 'black',label = 'Total Ejecta Deposit')\n",
    "\n",
    "\n",
    "ax.semilogy(dist_r,local/1000, linestyle = '--',color = 'dimgray',linewidth = 3, label = 'Local Material')\n",
    "ax.semilogy(dist_r,primary/1000,linestyle = ':', color = 'darkgrey',linewidth = 3, label = 'Primary Ejecta')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_ylabel('Thickness (km)', fontsize = 16)\n",
    "ax.set_xlabel('Distance from Imbrium impactor (km)',fontsize = 16)\n",
    "ax.legend(loc = 'lower left', fontsize = 18)\n",
    "ax.set_title('Modeled Imbrium Ejecta', fontsize = 20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5735ecea-9337-4936-b982-4f75213ff2bd",
   "metadata": {},
   "source": [
    "# References \n",
    "\n",
    "Haskin 1998\n",
    "Haskin 2003\n",
    "Xie 2020"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
